Computers & Industrial Engineering最新文献

{"title":"Integrated optimization of timetabling and berthing for dual-source trolleybus routes along corridors","authors":"Baoyu Hu ,&nbsp;Cheng Gao ,&nbsp;Wei Gao","doi":"10.1016/j.cie.2025.111066","DOIUrl":"10.1016/j.cie.2025.111066","url":null,"abstract":"<div><div>An integrated optimization approach for timetable and berth allocation has been proposed to address congestion issues at transportation nodes in common-route corridors for dual-source trolleybus systems. The study integrates travel times and the uncertainty of passenger demand to establish a bi-objective mixed-integer optimization model for minimizing the fleet sizes and required number of berths at common stations. By utilizing a Monte Carlo simulation to obtain the expected values of the fleet size and number of berths during actual route operations, the model can generate robust timetables and determine the number of berths at the common stations. The solving algorithm comprises a multi-objective particle swarm optimization with random weight variations. The results from a case study on four dual-source trolleybus routes in Beijing demonstrate that the model reduces the fleet size by 7.78% and number of berths at common stations by 11.11%. Consequently, this model can effectively reduce the long-term operational costs of bus companies and minimize the impacts of route operations on the road network.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111066"},"PeriodicalIF":6.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drone-assisted delivery optimization: Balancing time and cost with multiple truck routes for efficient service","authors":"Shalabh Singh","doi":"10.1016/j.cie.2025.111061","DOIUrl":"10.1016/j.cie.2025.111061","url":null,"abstract":"<div><div>Conventional last-mile delivery logistical systems remain overburdened despite large-scale optimization. This paper explores the usage of drones to complement road-based last-mile delivery systems to ensure more time-cost efficiency. The work also extends the truck shipment routes to the case of real-time multiple routes. A bi-objective mixed integer linear program is introduced, accompanied by an exact solution methodology utilizing multi-choice conic goal programming. This approach guarantees the generation of properly efficient points and effectively addresses smaller-scale problems. For larger instances, a three-step iterative heuristic is proposed that generates good time and cost solutions in a reasonable amount of time. Exhaustive computational analysis and an illustrative case suggest that substantial savings in both time and cost can be achieved compared to traditional frameworks.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111061"},"PeriodicalIF":6.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Multi-tier supply chain network design: A key towards sustainability and resilience” [Comput. Ind. Eng. 182 (2023) 109396]","authors":"Ahmed Mohammed ,&nbsp;Kannan Govindan ,&nbsp;Nasiru Zubairu ,&nbsp;Jacob Pratabaraj ,&nbsp;Ahmed Zainul Abideen","doi":"10.1016/j.cie.2025.111051","DOIUrl":"10.1016/j.cie.2025.111051","url":null,"abstract":"","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"204 ","pages":"Article 111051"},"PeriodicalIF":6.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerated acceptance sampling plan for degraded products based on inverse Gaussian process considering the acceleration factor uncertainty","authors":"Huiling Zheng ,&nbsp;Jun Yang ,&nbsp;Yu Zhao","doi":"10.1016/j.cie.2025.111052","DOIUrl":"10.1016/j.cie.2025.111052","url":null,"abstract":"<div><div>Acceptance sampling plan based on accelerated degradation tests, denoted as ADASP, is widely employed to verify the reliability of degraded products, including risk analysis, sampling plan design, and decision-making criterion determination. Most existing research focuses on identifying the optimal accelerated degradation test to improve the efficiency of ADASP, but often overlooks the differing objectives of producers and consumers regarding the acceptance index, which may result in the test plan failing to effectively meet their needs. To address this, based on the inverse Gaussian process, we propose a comprehensive accelerated degradation sampling plan by optimizing the parameter estimation accuracy while protecting their interests. Beyond the product quality risk, the acceleration factor (AF) uncertainty introduces additional risk to ADASP. Current studies primarily tackle AF uncertainty using probability distribution or interval. However, obtaining AF’s distribution is challenging, especially with limited prior knowledge and complex models. Therefore, apply the generalized pivotal quantity to derive a confidence interval of AF, and effectively manage this uncertainty-driven risk using significance level. Subsequently, a detailed decision-making criterion is derived by solving the risk constraint equations for both parties. Finally, simulation studies and case applications on springs are conducted to demonstrate the effectiveness of the proposed method.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111052"},"PeriodicalIF":6.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How does dual-credit policy regulate competitive fuel vehicle and new energy vehicle manufacturers? based on operational decision analysis under multiple scenarios","authors":"Junchong Pu ,&nbsp;Weide Chun","doi":"10.1016/j.cie.2025.111076","DOIUrl":"10.1016/j.cie.2025.111076","url":null,"abstract":"<div><div>This article investigates the remarkable impacts of the dual-credit policy (DCP) on fuel vehicle (FV) and new energy vehicle (NEV) automakers, and considers the competition for emission reduction between FVs and NEVs. We apply the game-theoretic approach to design four scenarios, i.e., no DCP, DCP to regulate NEV automakers, DCP to regulate FV automakers, and DCP to regulate dual automakers. The study reveals that increasing the credit trading price reduces the emission reduction level for FVs but not for NEVs. When the DCP regulates NEV automakers, the credit trading price positively correlates with the automaker’s profit. An increase in each NEV credit boosts the emission reduction level of NEVs and automakers’ profits. Conversely, an increase in the NEV credit ratio lowers the emission reduction level of FVs. The higher the credit trading price, each NEV credit and NEV credit ratio contribute to reducing the total carbon emission level. Meanwhile, the total carbon emission level is the lowest in the scenario of DCP regulating dual automakers, but the automakers’ profits will suffer some losses. Also, the scenario of DCP regulating NEV automakers to achieve the highest profits will result in a higher carbon emission level. Moreover, the main contribution of this study is to identify the business performance of DCP regulating single or dual automakers, while simultaneously pointing out the effective choices for DCP to promote the green operations of competing automakers. Our results suggest some novel management recommendations for automakers and governments that can contribute to enhancing the sustainability of the automotive industry.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111076"},"PeriodicalIF":6.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective dynamic feedback algorithm for solving the multi-drop three-dimensional multiple bin-size bin packing problem","authors":"Yi Liu,&nbsp;Xiaoyun Jiang","doi":"10.1016/j.cie.2025.111059","DOIUrl":"10.1016/j.cie.2025.111059","url":null,"abstract":"<div><div>Three-dimensional multiple bin-size bin packing problem (3D-MBSBPP) is a crucial component of logistics and transportation systems. Prior studies focus on 3D-MBSBPP in cases where unloading is not considered. However, as the variety of vehicle types and the number of customers’ cargo increase, it not only reduces the efficiency of cargo loading and unloading but also increases the diversified needs of companies. To address these difficulties, more efficient methods are imperatively required. In this study, we construct a novel model for the multi-drop 3D-MBSBPP, which incorporates three objectives: maximizing vehicle space utilization rate, minimizing vehicle usage costs and spatial blockage index, while considering some practical constraints. Given the high complexity of this model, we propose a novel multi-objective dynamic feedback algorithm to solve it, which consists of three stages. Among them, Stage 1 focuses on optimizing the placement relationship between cargoes with different unloading sequences; Stage 2 dynamically adjusts and optimizes the vehicle types; and Stage 3 further optimizes the quality of the solution. More importantly, the proposed algorithm helps decision makers with the trade-off between multiple objectives. We demonstrate the effectiveness of the proposed algorithm through comparative experiments on benchmark instances and apply it to solve muti-drop 3D-MBSBPP. The results indicate that based on generating smaller spatial blockage index, the proposed algorithm can improve the average vehicle space utilization rate and reduce the average vehicle usage costs. This demonstrates the superiority of the proposed algorithm in solving the multi-drop 3D-MBSBPP.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111059"},"PeriodicalIF":6.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A genetic programming based reinforcement learning algorithm for dynamic hybrid flow shop scheduling with reworks under general queue time limits","authors":"Hyeon-Il Kim,&nbsp;Yeo-Reum Kim,&nbsp;Dong-Ho Lee","doi":"10.1016/j.cie.2025.111062","DOIUrl":"10.1016/j.cie.2025.111062","url":null,"abstract":"<div><div>This study addresses a hybrid flow shop scheduling problem in which each job with non-zero arrival time is reworked after a rework setup is done when one of its general queue time limits between two arbitrary stages is violated. The problem is to determine the allocations of jobs to machines at each stage and the start times of jobs and rework setups/operations, if occur, with the objective of minimizing total tardiness. After representing the problem as a mixed integer programming model, a genetic programming based deep reinforcement learning (GP-DRL) algorithm is proposed. The algorithm consists of two phases: (a) generation of superior hyper priority rules using a variable neighborhood search based genetic programming (VNS-GP) algorithm; and (b) construction of a complete schedule by applying one of the superior hyper rules at each scheduling point by a Deep Q-network with state features, actions and rewards designed using the characteristics of the problem. Simulation experiments were done on a number of test instances, and the results can be summarized as follows. First, the superior hyper priority rules generated by the VNS-GP algorithm outperform the conventional ones in overall averages. Second, the superior hyper rule based GP-DRL algorithm dominates the conventional rule based DRL algorithm.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111062"},"PeriodicalIF":6.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing machine tool predictive maintenance: A dual-model approach integrating improved deep autoencoders and graph attention network","authors":"Changchun Liu ,&nbsp;Dunbing Tang ,&nbsp;Haihua Zhu ,&nbsp;Qixiang Cai ,&nbsp;Zequn Zhang ,&nbsp;Qingwei Nie","doi":"10.1016/j.cie.2025.111048","DOIUrl":"10.1016/j.cie.2025.111048","url":null,"abstract":"<div><div>Along with the increasing amount and complication of machine tools, various machine faults appear and the stability of the inherent manufacturing process may be threatened. On the one hand, high-dimensional data is difficult to be effectively used to accurately predict the timing of a fault. On the other hand, even with a rough estimate of the fault time, it is difficult for maintenance personnel to quickly find the root cause of the fault during the stage of predictive maintenance. The fundamental reason is the lack of cognition and reasoning about the correlation of a large amount of underlying knowledge in fault prediction and maintenance. To address this issue, a dual-model driven predictive maintenance approach is proposed by using deep autoencoder and graph attention neural network to enhance the stability of machine tools. Firstly, a system architecture of the proposed dual-model driven predictive maintenance is designed with fault data acquisition of machine tools, fault prediction model driven by CNN-BiLSTM-Autoencoder, and Graph Attention Network-driven maintenance service recommendation, which can be illustrated minutely as follows. Based on the acquires high-dimensional fault data, a CNN-BiLSTM-Autoencoder-based fault prediction model is proposed for machine tools, which can capture the underlying relationships among fault features to calculate accurate fault prediction results. Based on the accurate fault prediction result, an effective maintenance service recommendation approach is proposed based on the improved Graph Attention Network combined with Neural Tensor Networks, which can capture more complex relationships among fault causes and maintenance services. Based on this, maintenance services that well match maintenance requirements for actual faults can be recommended. Finally, comparative experiments are conducted within a machining workshop featuring a diversity of machine tools, which confirm that the proposed approach can exceed traditional methods in terms of fault prediction and maintenance recommendation performance.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111048"},"PeriodicalIF":6.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel method of detection of noisy samples in high-dimensional sequential data considering both attribute and label noise","authors":"Liang Chang ,&nbsp;Lu-Xin Guan ,&nbsp;Enrico Zio ,&nbsp;Yan-Hui Lin","doi":"10.1016/j.cie.2025.111064","DOIUrl":"10.1016/j.cie.2025.111064","url":null,"abstract":"<div><div>The high-dimensional sequential data available across various industrial scenarios may be contaminated with both attribute and label noise, hindering the establishment of accurate deep learning-based prediction models. The existing noise detection methods can only detect one type of noise. Conversely, in this article, a novel noisy samples detection method is proposed to detect both types of noise simultaneously through generative learning. An enhanced variational recurrent prediction model (EVRPM) is proposed to model the log-likelihood of samples, which incorporates a label predictor and an auxiliary task into the variational recurrent neural network. Moreover, an iterative detection process is adopted to refine EVRPM training and enhance noisy sample detection, which is particularly beneficial for low-quality datasets. A prediction model with higher prediction accuracy can be obtained using the refined dataset. The effectiveness and superiority of the proposed method are verified using both public and real experimental datasets.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111064"},"PeriodicalIF":6.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability optimization of non-linear RRAP with cold standby through HPSOTLBO","authors":"Shivani Choudhary ,&nbsp;Mangey Ram ,&nbsp;Nupur Goyal","doi":"10.1016/j.cie.2025.111045","DOIUrl":"10.1016/j.cie.2025.111045","url":null,"abstract":"<div><div>The cold standby reliability redundancy allocation problems (RRAP) with nonlinear constraints have significant challenges in optimization due to their complexity. This work aims to address these problems by developing a hybrid optimization technique called hybrid particle swarm optimization with teaching–learning-based optimization (HPSOTLBO). In this study, the basic methodology of the proposed research is a metaheuristic approach. Its primary objective is to maximize system reliability by optimizing redundancy and component reliability through the balance between local exploitation and global exploration. The HPSOTLBO algorithm combines the robust global search capability of particle swarm optimization (PSO) with the rapid convergence features of teaching–learning-based optimization (TLBO). The hybrid approach, TLBO dynamically updates the PSO’s searching processes, enhancing its effectiveness in locating optimal solutions. The algorithm is tested on three benchmark problems for reliability optimization with cold standby strategy to demonstrate its practical utility. Computational experiments reveal that HPSOTLBO consistently outperforms both PSO and TLBO individually, as well as several previously utilized metaheuristic approaches. According to outcomes, HPSOTLBO provides a strong framework for nonlinear RRAP. Further, the statistical analysis using the Friedman ranking test and Wilcoxon sign-rank test validates the algorithm’s superior efficiency. Finally, the study shows that HPSOTLBO is an effective technique for resolving RRAP with a cold standby strategy.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 111045"},"PeriodicalIF":6.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}